适应性知识转移用于跨学科冷启动知识追踪
Adaptive Knowledge Transfer for Cross-Disciplinary Cold-Start Knowledge Tracing
AI总结：
本文提出基于混合专家和对抗生成网络的跨学科冷启动知识追踪框架，通过聚类和门控机制提升跨学科知识转移效果，缓解小样本问题。
摘要：
跨学科冷启动知识追踪（CDCKT）面临关键挑战：目标学科中学生互动数据不足，导致无法有效建模知识状态和预测表现。现有跨学科方法依赖学科间重叠实体通过简单映射函数进行知识转移，但存在两个关键限制：（1）现实场景中重叠实体稀缺，（2）简单映射无法充分捕捉跨学科知识复杂性。为克服这些挑战，我们提出了基于混合专家和对抗生成网络的跨学科冷启动知识追踪框架。我们的方法包含三个关键组件：首先，我们预训练源学科模型并将学生知识状态聚类为K类。其次，这些聚类属性通过门控机制引导混合专家网络，作为跨领域映射桥梁。第三，对抗判别器通过拉近同属性学生特征并推远不同属性特征，有效缓解小样本限制。我们验证了方法在20个极端跨学科冷启动场景中的有效性。
英文摘要：
Cross-Disciplinary Cold-start Knowledge Tracing (CDCKT) faces a critical challenge: insufficient student interaction data in the target discipline prevents effective knowledge state modeling and performance prediction. Existing cross-disciplinary methods rely on overlapping entities between disciplines for knowledge transfer through simple mapping functions, but suffer from two key limitations: (1) overlapping entities are scarce in real-world scenarios, and (2) simple mappings inadequately capture cross-disciplinary knowledge complexity. To overcome these challenges, we propose Mixed of Experts and Adversarial Generative Network-based Cross-disciplinary Cold-start Knowledge Tracing Framework. Our approach consists of three key components: First, we pre-train a source discipline model and cluster student knowledge states into K categories. Second, these cluster attributes guide a mixture-of-experts network through a gating mechanism, serving as a cross-domain mapping bridge. Third, an adversarial discriminator enforces feature separation by pulling same-attribute student features closer while pushing different-attribute features apart, effectively mitigating small-sample limitations. We validate our method's effectiveness across 20 extreme cross-disciplinary cold-start scenarios.
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